Optimal Design of Heater with Magnetic Field Self-suppression Based on Genetic Algorithm
The ultra-low magnetic noise (<10nT) of alkali metal heating technique is critical for achieving ultra-high sensitivity in spin-exchange relaxation-free atomic magnetometers. In this study,a multi-objective optimization and design method for a magnetic-field self-suppression heater based on a genetic algorithm was proposed. A novel objective function model based on the Biot-Savart law was derived,and four types of parameters were used for the optimization objectives (a total of 18),including the length,width,thickness,and current direction of the heating wire,in order to obtain the best magnetic self-suppression performance from the heater. Using the finite element analysis method,the magnetic field distribution and temperature distribution in the target region were simulated and analyzed,and the results showed that the heater produced an average magnetic field of 0.02nT/mA and an average temperature of 180.34℃ in the center of the target region. Experimental tests confirmed that the magnetic flux density in the target region fell within the range of 0.13～0.14nT/mA,which indicated that the heater had a better self-suppressing performance for the magnetic field. This work contributes to further enhancing the performance of atomic magnetometers.
spin-exchange relaxation-free (SERF) atomic magnetometersmagnetic field self-suppressiongenetic algorithmBiot-Savart law
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维普
